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Research project

Historical Geospatial Networks of Night Train Services in Europe

Project overview

Long-term time series analysis of transport network development has the potential to provide important insights into the factors and processes which influence how transport systems evolve. However, the relevant datasets for such analysis are seldom available in electronic formats which permit easy analysis. This is a particular challenge for data on public transport services, where printed timetables can form the only comprehensive source of information on historic service patterns. This research is investigating the relative efficacy of three methods for extracting such data from historic railway timetables, with a particular focus on overnight train services in Europe. The methods tested are: 1) manual geocoding of train routes on an individual basis; 2) crowd-sourced geocoding of train running times in GTFS format; and 3) OCR analysis of scanned timetable data. Test implementations of all three methods are being undertaken, to allow comparison of a) the time and researcher effort required and b) the accuracy and level of detail provided by the outputs. The outputs from the data extraction process are then being used to investigate the relative importance of the various factors which have influenced the development of overnight train services over time, and to consider how such networks might continue to develop in the future.


Lead researcher

Professor Simon Blainey PhD, FRGS, FHEA, MCIHT, CMILT

Professor of Sustainable Transport

Research interests

  • Rail demand and operations modelling
  • GIS and transport
  • Transport decision support systems
Other researchers

Dr Marcus Young PhD, FRGS

Research Fellow in GIS Transp Eng Data

Research interests

  • Application of GIS methods and techniques to research relating to public transport and active travel.
  • Development of open source tools for transport planning applications.
  • Rail demand modelling and appraisal with a focus on developing improved aggregate models to forecast demand for new railway stations.

Dr John Armstrong

Snr Res Fellow in Engineering Economics

Collaborating research institutes, centres and groups

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